Neural network circuitry for motors

ABSTRACT

An apparatus for driving a motor includes a plurality of neurons of neural network circuitry and motor circuitry. The plurality of neurons are configured to generate a cycle value based on a target speed, based on a speed value associated with the motor at a particular time, and based on a current value associated with the motor at the particular time. The plurality of neurons is configured to be trained to generate the cycle value to minimize an error between the cycle value and a training cycle value for each training vector of a plurality of training vectors. The apparatus is configured to have generated the plurality of training vectors. The motor circuitry is configured to control, based on the cycle value, a set of switching elements to drive the motor.

TECHNICAL FIELD

This disclosure relates to electric motors, and more particularly, totechniques and circuits associated with electric motors.

BACKGROUND

Operation of electric motors may be performed by a motor circuitry. Themotor circuitry may control a rotor rotation of the motor. For example,the motor circuitry may drive current at the motor in order to controlthe motor and regulate a speed of the motor.

SUMMARY

The disclosure describes techniques, devices, and systems for improvingoperation of motor circuitry for driving a motor. Rather than relying oncomplex computing devices and human input to generate training vectorsto train neurons, the system may be configured to generate trainingvectors in real-time. In this way, a neural network controlling themotor circuitry may be trained using training vectors generated by thesystem itself, which may reduce the complexity of the procedure togenerate training sets for the neural network circuitry compared tosystems relying on human input and/or complex computing devices togenerate training vectors.

In some examples, rather than relying on a static training of neurons,neural network circuitry may be configured to adapt based on error(e.g., a difference) between an actual setup and an ideal setup, whichmay improve an accuracy of neural network compared to motor controllersusing programmed motor controllers and/or statically implemented neuralnetworks.

In one example, the disclosure is directed to an apparatus for driving amotor, the apparatus including a plurality of neurons of neural networkcircuitry and motor circuitry. The plurality of neurons of neuralnetwork circuitry is configured to generate a cycle value based on atarget speed, based on a speed value associated with the motor at aparticular time, and based on a current value associated with the motorat the particular time. The plurality of neurons is configured to betrained to generate the cycle value to minimize an error between thecycle value and a training cycle value for each training vector of aplurality of training vectors. The apparatus is configured to havegenerated the plurality of training vectors. The motor circuitry isconfigured to control, based on the cycle value, a set of switchingelements to drive the motor.

In another example, the disclosure is directed to a method for driving amotor, the method including generating, by a plurality of neurons ofneural network circuitry of an apparatus for driving the motor, a cyclevalue based on a target speed, based on a speed value associated withthe motor at a particular time, and based on a current value associatedwith the motor at the particular time. The plurality of neurons isconfigured to be trained to generate the cycle value to minimize anerror between the cycle value and a training cycle value for eachtraining vector of a plurality of training vectors. The apparatus isconfigured to have generated the plurality of training vectors. Themethod further includes controlling, by motor circuitry and based on thecycle value, a set of switching elements to drive the motor.

In one example, the disclosure is directed to an apparatus for driving amotor, the apparatus including a set of switching elements, a pluralityof neurons of neural network circuitry, and motor circuitry. Theplurality of neurons of neural network circuitry is configured togenerate a cycle value based on a target speed, based on a speed valueassociated with the motor at a particular time, and based on a currentvalue associated with the motor at the particular time. The plurality ofneurons is configured to be trained to generate the cycle value tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors. The apparatusis configured to have generated the plurality of training vectors. Themotor circuitry is configured to control, based on the cycle value, theset of switching elements to drive the motor.

In another example, the disclosure is directed to an apparatuscomprising: means for generating, with a plurality of neurons, a cyclevalue based on a target speed, based on a speed value associated withthe motor at a particular time, and based on a current value associatedwith the motor at the particular time. The plurality of neurons isconfigured to be trained to generate the cycle value to minimize anerror between the cycle value and a training cycle value for eachtraining vector of a plurality of training vectors. The apparatus isconfigured to have generated the plurality of training vectors. Theapparatus further includes means for controlling, based on the cyclevalue, a set of switching elements to drive the motor.

In one example, the disclosure is directed to an apparatus for driving amotor, the apparatus including a first plurality of neurons of neuralnetwork circuitry, motor circuitry, and a second plurality of neurons ofthe neural network circuitry. The first plurality of neurons of neuralnetwork circuitry is configured to generate a first cycle value based ona target speed. The motor circuitry is configured to control, based onthe first cycle value, a set of switching elements to drive the motor.The second plurality of neurons of the neural network circuitry isconfigured to train the second plurality of neurons to generate, basedon a resulting speed value for the motor that occurs when the motorcircuitry has controlled the set of switching elements to drive themotor based on the first cycle value, a second cycle value to minimize adifference between the second cycle value and the first cycle value.

In another example, the disclosure is directed to a method for driving amotor, the method including generating, by a first plurality of neuronsof neural network circuitry, a first cycle value based on a target speedand controlling, by motor circuitry and based on the first cycle value,a set of switching elements to drive the motor. The method furtherinclude training, by the neural network circuitry, a second plurality ofthe neurons of neural network circuitry to generate, based on aresulting speed value for the motor that occurs when the motor circuitryhas controlled the set of switching elements to drive the motor based onthe first cycle value, a second cycle value to minimize a differencebetween the second cycle value and the first cycle value.

In one example, the disclosure is directed to an apparatus for driving amotor, the apparatus including motor circuitry, a first plurality ofneurons of neural network circuitry, and a second plurality of neuronsof neural network circuitry. The motor circuitry is configured tocontrol a set of switching elements to drive the motor based on a firstcycle value. The first plurality of neurons of the neural networkcircuitry is configured to generate a plurality of training vectors,train, when a predetermined variable mechanical load is applied to themotor, the first plurality of neurons to generate the first cycle valueto minimize a difference between the first cycle value and the trainingcycle value for each training vector of the plurality of trainingvectors, and generate the first cycle value based on a target speed. Thesecond plurality of neurons of the neural network circuitry isconfigured to train, when the predetermined variable mechanical load isnot applied to the motor, the second plurality of neurons to generate,based on a resulting speed value for the motor that occurs when themotor circuitry has controlled the set of switching elements to drivethe motor based on the first cycle value, a second cycle value tominimize a difference between the second cycle value and the first cyclevalue.

In another example, the disclosure is directed to an apparatuscomprising: means for generating, with a first plurality of neurons ofneural network circuitry, a first cycle value based on a target speedand means for controlling, based on the first cycle value, a set ofswitching elements to drive the motor. The apparatus further includesmeans for training a second plurality of the neurons of neural networkcircuitry to generate, based on a resulting speed value for the motorthat occurs when the motor circuitry has controlled the set of switchingelements to drive the motor based on the first cycle value, a secondcycle value to minimize a difference between the second cycle value andthe first cycle value.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example first systemconfigured for driving a motor using neural network circuitry, inaccordance with one or more techniques of this disclosure.

FIG. 2 is a block diagram illustrating an example second systemconfigured for driving a motor, in accordance with one or moretechniques of this disclosure.

FIG. 3 is a block diagram illustrating an example system configured fortraining a plurality of neurons, in accordance with one or moretechniques of this disclosure.

FIG. 4 is a block diagram illustrating an example plurality of neurons,in accordance with one or more techniques of this disclosure.

FIG. 5 is a block diagram illustrating an example setup for training aplurality of neurons, in accordance with one or more techniques of thisdisclosure.

FIG. 6 is a conceptual diagram illustrating an example resistor boardfor training a plurality of neurons, in accordance with one or moretechniques of this disclosure.

FIG. 7 is a block diagram illustrating an example first systemconfigured for driving a motor using a first plurality of neurons and asecond plurality of neurons, in accordance with one or more techniquesof this disclosure.

FIG. 8 is a block diagram illustrating an example system configured fortraining a second plurality of neurons, in accordance with one or moretechniques of this disclosure.

FIG. 9 is a flow diagram for training a second plurality of neurons, inaccordance with one or more techniques of this disclosure.

FIG. 10 is a graph illustrating of a mean squared error during training,in accordance with one or more techniques of this disclosure.

FIG. 11 is a graph illustrating a back-electromagnetic force (BEMF)voltage tracking, in accordance with one or more techniques of thisdisclosure.

FIG. 12 is a graph illustrating a BEMF voltage tracking during loadchanges, in accordance with one or more techniques of this disclosure.

FIG. 13 is a flow diagram for driving a motor using neural networkcircuitry, in accordance with one or more techniques of this disclosure.

FIG. 14 is a flow diagram for driving a motor using a first plurality ofneurons and a second plurality of neurons, in accordance with one ormore techniques of this disclosure.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description, drawings, and claims.

DETAILED DESCRIPTION

Motor regulation may be performed with a regulator, which may includeproportional and an integral (PI) control. The regulator may generate adigital modulation signal (e.g., a pulse-width modulation (PWM) signal,a pulse density modulation (PDM) signal, a pulse-code modulation (PCM)signal, a sigma-delta modulation, or another type of digital modulationsignal). The digital modulation signal may switch (e.g., activate ordeactivate) switching elements (e.g., transistors of a motor bridge).The motor regulation may use the speed of the motor (e.g, using aback-electromagnetic force (BEMF) voltage) and the mechanical torque inform of the motor current as a feedback. The regulator may use thesefeedback signals (e.g., the BEMF voltage and/or the motor current) torealize either a constant speed or a constant torque regulation.

While this disclosure refers to systems configured to apply constantspeed regulation, techniques described herein may be applied to motorsystems configured to apply a constant torque. In the example ofconstant speed, the regulator may apply constant speed regulation usingan algorithm programed by a human user (e.g., in the C programminglanguage) that is compiled and stored in memory (e.g., a flash memory ofa microcontroller). In some examples, hardware may interface a motorbridge with the control signals from a system-on-chip, which iscontrolled by microcontroller software.

Some systems implementing a neural network may perform a process ofgenerating, by a human user, training vectors for a particularapplication. The training vectors may be output to high-performancehardware (e.g., using a graphics processing unit or GPU) to “train”neurons of neural network circuitry. However, such systems may rely onhuman interaction as well as high-performance hardware that is discretefrom a micro-controller integrated in a system-on-chip of a motorcontroller. Moreover, the system-on-chip of the motor controller mayhave limited random access memory, which would not be sufficient tostore the various training vectors for training the neurons.

Rather than relying on a human user to generate training vectors and/orrelying on high-performance hardware that is discrete from a motorcontroller to train neurons for driving a motor, techniques describedherein may configure the motor controller itself to train neurons fordriving a motor. The motor controller may receive the same or differentinput signals used by an algorithm programed by a human user (e.g., theBEMF voltage and/or the motor current) and may generate as an output avalue for the cycle value (e.g., a PWM duty cycle) which may control themotor speed. In other words, the motor controller may not be programmedby a human user and may not be preconfigured to a particular motorapplication. Instead, the motor controller itself may be configured tolearn how to control the motor, which may be a more optimal control thancan be achieved by human programming.

The disclosure includes aspects to realize the neural network trainingand execution using deep learning techniques. Techniques describedherein may be adapted and in some examples, may be configured to work ina low-power and/or low-performance environment (e.g., amicrocontroller). For example, neural network circuitry may beconfigured to implement a minimal set of neurons to achieve a sufficientregulation quality. In some examples, neural network circuitry of amotor controller may include a number of neurons (e.g., less than 10neurons, less than 50 neurons, or another number of neurons) that areadapted for a motor controller, such as, for example, a microcontroller,rather than a predetermined number of neurons (e.g., greater than 100neurons, greater than 1,000 neurons, or another predetermined number ofneurons) adapted for high-performance hardware discrete from the motorcontroller. In some examples, a motor controller may apply a maximumdelta operation (also referred to herein as “MaxDelta operation”) tostabilize speed regulation by reducing a maximum allowable signal jump.For instance, the motor controller may be configured to limit a maximumchange in speed at a motor such that a control response of the motorcontroller is dampened, which may help to improve a stability of themotor controller. In some examples, the neural network circuitry may beconfigured to use a 32-bit float format to represent values (e.g., aspeed value, a current value, a cycle value, and/or another value),which may help to reduce or eliminate quantization of the values.

Additionally, or alternatively, in some examples, techniques describedherein may include a neural network configured to learn on the fly whilea system is in-service and after the neural network circuitry hastrained a first plurality of neurons to regulate a motor. For example,in some cases, the neural network circuitry may be configured to adapt asecond plurality of neurons based on an observed resulting speed valueat the motor while the motor is regulated (e.g., to a speed or torque)by the first plurality of neurons. This adaption may account for variousmechanical and/or electrical changes (e.g., aging of the motor and/orother changes), which may improve an accuracy of the system compared tomotor controllers using programmed motor controllers and/or staticallyimplemented neural networks.

FIG. 1 is a block diagram illustrating an example first system 100configured for driving a motor 106 using neural network circuitry 110,in accordance with one or more techniques of this disclosure. System 100(also referred to herein as a “motor controller” or “an apparatus fordriving the motor”) may include supply 101, neural network circuitry110, motor circuitry 112, set of switching elements 114, and motor 106.While FIG. 1 shows system 100 as having separate and distinctcomponents, some components may be combined or further separated. Forexample, motor circuitry 112 and neural network circuitry 110 may becombined. For instance, motor circuitry 112 and neural network circuitry110 may be formed into a system on a chip (SOC) and/or an integratedcircuit (IC). In some examples, however, motor circuitry 112 may beseparate and distinct from neural network circuitry 110.

Supply 101 may be configured to provide electrical power to one or moreother components of system 100. For instance, supply 101 may beconfigured to supply power to motor 106. In some examples, supply 101may be a battery which may be configured to store electrical energy.Examples of batteries may include, but are not limited to,nickel-cadmium, lead-acid, nickel-metal hydride, nickel-zinc,silver-oxide, lithium-ion, lithium polymer, any other type ofrechargeable battery, or any combination of the same. In some examples,supply 101 may be an output of a power converter or power inverter. Forinstance, supply 101 may be an output of a direct current (DC) to DCpower converter, an alternating current (AC) to DC power converter, a DCto AC power inverter, and the like. In some examples, the input powersignal provided by supply 101 may be a DC input power signal. Forinstance, supply 101 may be configured to provide a DC input powersignal in the range of ˜5 VDC to ˜40 VDC.

Motor 106 may comprise a DC brushless motor, also known as a brushlessDC motor, or simply “BLDC motor.” In some examples, motor 106 maycomprise a DC-excited motor. For example, motor 106 may include a shaft,rotor, stator, and permanent magnet. A permanent magnet may be mountedon or in the rotor. In some examples, the permanent magnet may besurface mounted to the rotor, inset in the rotor, or buried within therotor. In some examples, the permanent magnet may be an interior magnet.The permanent magnet may include rare-earth elements, such asNeodymium-Iron-Boron (NdFeB), Samarium-Cobalt (SmCo), or Ferriteelements (e.g., Barium (Ba) or Strontium (Sr)). In some examples, thepermanent magnet may include a protective coating such as a layer ofGold (Au), Nickel (Ni), Zinc (Zn), other elements, and/or othercompounds.

Motor circuitry 112 may be configured to control, based on a cycle valueoutput from neural network circuitry 110, set of switching elements 114to drive motor 106. For example, motor circuitry 112 may includecircuitry configured to generate, based on the cycle value (e.g., a dutycycle value), a digital modulated signal. Examples of a digitalmodulated signal may include, for example, a pulse width modulated (PWM)signal, a pulse density modulation (PDM) signal, a pulse-code modulation(PCM) signal, a sigma-delta modulation, or another digital modulatedsignal. Examples of a cycle value may include, for example, a PWM cyclevalue, a PDM cycle value, a PCM cycle value, a sigma-delta modulationcycle value, or another cycle value.

Motor circuitry 112 may be configured to drive, based on the digitalmodulated signal, set of switching elements 114 to operate in at least afirst switching state and a second switching state. During the firstswitching state, set of switching elements 114 may electrically couple afirst terminal of motor 106 and a first supply terminal (e.g., apositive terminal, a negative terminal or a ground terminal) of supply101 and electrically couple a second terminal of the motor and a secondsupply terminal (e.g., a negative terminal, a positive terminal or aground terminal) of supply 101. During the second switching state, setof switching elements 114 may electrically couple the second terminal ofthe motor and the first supply terminal (e.g., a positive terminal or anegative terminal) of supply 101 and may electrically couple the firstterminal of the motor and the second supply terminal (e.g., a negativeterminal or a positive terminal) of supply 101.

Motor circuitry 112 may include, for example, a microcontroller on asingle integrated circuit containing a processor core, memory, inputs,and outputs. For example, motor circuitry 112 may include one or moreprocessors, including one or more microprocessors, digital signalprocessors (DSPs), application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs), or any other equivalentintegrated or discrete logic circuitry, as well as any combinations ofsuch components. The term “processor” or “processing circuitry” maygenerally refer to any of the foregoing logic circuitry, alone or incombination with other logic circuitry, or any other equivalentcircuitry. In some examples, motor circuitry 112 may be a combination ofone or more analog components and one or more digital components.

As shown in FIG. 1, set of switching elements 114 may include fourswitching elements. In other examples, set of switching elements 114 mayinclude fewer (e.g., one switching element, two switching elements, orthree switching elements) or additional switching elements (e.g., morethan four switching elements) than shown in the example of FIG. 1.Examples of switching elements may include, but are not limited to, asilicon-controlled rectifier (SCR), a Field Effect Transistor (FET), anda bipolar junction transistor (BJT). Examples of FETs may include, butare not limited to, a junction field-effect transistor (JFET), ametal-oxide-semiconductor FET (MOSFET), a dual-gate MOSFET, aninsulated-gate bipolar transistor (IGBT), any other type of FET, or anycombination of the same. Examples of MOSFETS may include, but are notlimited to, a depletion mode p-channel MOSFET (PMOS), an enhancementmode PMOS, depletion mode n-channel MOSFET (NMOS), an enhancement modeNMOS, a double-diffused MOSFET (DMOS), any other type of MOSFET, or anycombination of the same. Examples of BJTs may include, but are notlimited to, PNP, NPN, heterojunction, or any other type of BJT, or anycombination of the same. It should be understood that switching elementsmay be high-side or low-side switching elements. Additionally, switchingelements may be voltage-controlled and/or current-controlled. Examplesof current-controlled switching elements may include, but are notlimited to, gallium nitride (GaN) MOSFETs, BJTs, or othercurrent-controlled elements.

Neural network circuitry 110 may include a plurality of neurons 102configured to generate a cycle value (e.g, a duty cycle value) based ona target speed, based on a speed value associated with motor 106 at aparticular time, and based on a current value associated with motor 106at the particular time. As shown, neural network circuitry 110 mayreceive the target speed, the speed value, and the current value. Forinstance, neural network circuitry 110 may be configured to, with thespeed circuitry, generate the speed value based on a measurement of aBEMF voltage at motor 106 and/or using an external sensor, for example,a Hall sensor, or another sensor configured to deliver a voltage and/ora digital value (e.g., not a current) indicating a speed at motor 106.Neural network circuitry 110 may include, for example, a microcontrolleron a single integrated circuit containing a processor core, memory,inputs, and outputs. For example, neural network circuitry 110 mayinclude one or more processors, including one or more microprocessors,DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logiccircuitry, as well as any combinations of such components. In someexamples, neural network circuitry 110 may be a combination of one ormore analog components and one or more digital components.

A plurality of neurons 102 may be configured to be trained to generatethe cycle value to minimize an error between the cycle value and atraining cycle value for each training vector of a plurality of trainingvectors. For example, plurality of neurons 102 may be pre-configured tohave been trained to generate a cycle value using techniques describedherein. In some example, plurality of neurons 102 may not have beentrained and may be configured to perform training to generate a cyclevalue using techniques described herein. For instance, plurality ofneurons 102 may be untrained and may be configured to perform trainingto generate the cycle value to minimize an error between the cycle valueand a training cycle value for each training vector of a plurality oftraining vectors.

In some systems, high-performance hardware (e.g., using a graphicsprocessing unit discrete from system 100) may train plurality of neurons102 of neural network circuitry 110. In such systems, a human user maygenerate a set of training vectors. For example, the human user maydetermine, for each training vector of a set of training vectors, atraining cycle value for a target speed, a current value, and a speedvalue. In some cases, the human user may run motor 106 at a desiredload. In such cases, the human user may determine an operating rotorspeed value at motor 106 and an operating current value at motor 106.The human user may then drive motor circuitry 112 to output a trainingcycle value. In this example, the human user may determine (e.g.,measure) a resulting rotor speed at motor 106. The human user maygenerate a single training vector as including the training cycle value,the operating rotor speed value as a speed value, the operating currentvalue as a current value, and the resulting speed value as a targetspeed. In this example, the human user may select different startingmotor speeds, different loads at motor 106, different cycle values,and/or other parameters to generate the entire set of training vectors.With a database of data representing the set of training vectors, thehuman user may batch train plurality of neurons 102 using all of thetraining vectors. In this way, the high-performance hardware may trainplurality of neurons 102 using the human generated training vectors.However, such systems may rely on human interaction and/orhigh-performance hardware, which may increase a cost to implement themotor controller.

In accordance with the techniques of the disclosure, system 100 may beconfigured to have generated a plurality of training vectors. Forexample, system 100 may, without human interaction, have selecteddifferent starting motor speeds, different loads at motor 106, differentcycle values, and/or other parameters to have generated training vectorsof the set of training vectors. In some examples, system 100 may,without human interaction, be configured to select different startingmotor speeds, different loads at motor 106, different cycle values,and/or other parameters to generate training vectors of the set oftraining vectors.

Rather than generating a large database of data including all of theinformation to train plurality of neurons 102, neural network circuitry110 may have trained or may be configured to train plurality of neurons102 in real-time. For example, neural network circuitry 110 may drivemotor 106 to a training cycle value and generate a training vector usinga resulting rotor speed as a target speed. In this example, neuralnetwork circuitry 110 may train plurality of neurons 102 using thetraining vector in-real time. That is, neural network circuitry 110 maytrain plurality of neurons 102 without waiting for additional trainingvectors to be generated.

System 100 may reuse data (e.g., speed values, current values, cyclevalues, or other values) to generate additional training vectors. Thatis, system 100 may generate a new training vector using information(e.g., a speed value, a current value, a cycle value, or other values)of a previous training vector and/or reuse memory allocated to store aprevious training vector for storing a first training vector. In thisway, system 100 may operate with less memory storage consumptioncompared to systems that batch train neurons using multiple trainingvectors.

FIG. 2 is a block diagram illustrating an example second system 200configured for driving a motor 206 using neural network circuitry, inaccordance with one or more techniques of this disclosure. Plurality ofneurons 202, motor circuitry 212, and motor 206 may comprise examples ofplurality of neurons 102, motor circuitry 112, and motor 106 of FIG. 1.As shown, the example of FIG. 2 further includes maximum speed deltacircuitry 203, current circuitry 220, and speed circuitry 222.

Maximum speed delta circuitry 203 may be configured to receive areference speed and determine a target speed based on the referencespeed, a speed value associated with motor 206 at the particular time,and one or more previous speed values associated with motor 206 at oneor more previous times that occur before the particular time. Forexample, maximum speed delta circuitry 203 may generate the target speedas W1*ω(t)+W2*ω(t−1)+W3*ω(t−2), where W1 is a first weight value, W2 isa second weight value, W3 is a third weight value, ω(t) is a speed valueat the particular time (t), ω(t−1) is a speed value at a first previoustime, and ω(t−2) is a speed value at a second previous time. Forinstance, maximum speed delta circuitry 203 may generate the targetspeed as 0.5*ω(t)+0.3*ω(t−1)+0.1*(1)(t−2).

Current circuitry 220 may be configured to determine a current value ata particular time (i(t)), a first previous time (i(t−1)), and a secondprevious time (i(t−2)). Current circuitry 220 may include, for example,a Hall effect sensor configured to generate a voltage which isproportional to a magnetic flux density at a rotor or stator of motor206. The current values (e.g., i(t), i(t−1), i(t−2), or other currentvalues) may be proportional to a torque generated by motor 206. Forexample, current circuitry 220 (or other circuitry of system 200) may beconfigured multiply the measured current at motor 206 and apredetermined factor (e.g., a torque constant) to determine a torquegenerated by motor 206. While the example of FIG. 2 stores three currentvalues, other examples may store fewer current values (e.g., one currentvalue or two current values) or store more current values (e.g., morethan three current values).

Speed circuitry 222 may be configured to determine a speed value at aparticular time (ω(t)), a first previous time (ω(t−1)), and a secondprevious time (ω(t−2)). Speed circuitry 222 may measure aback-electromagnetic force (BEMF) voltage at motor 206 and at theparticular time. In this example, speed circuitry 222 may be configuredto determine the speed value associated with the motor at the particulartime based on the back-electromagnetic force voltage. For example, speedcircuitry 222 (or other circuitry of system 200) may be configuredmultiply the measured BEMF voltage at motor 206 and a predeterminedfactor (e.g., a back EMF constant) to generate a speed value. While theexample of FIG. 2 stores three speed values, other examples may storefewer speed values (e.g., one speed value or two speed values) or storemore speed values (e.g., more than three speed values).

In accordance with the techniques of the disclosure, plurality ofneurons 202 may generate the cycle value based on a target speed outputby maximum speed delta circuitry 203, one or more current values (e.g.,i(t), i(t−1), i(t−2), and/or other current values), one or more speedvalues (e.g., ω(t), ω(t−1), ω(t−2), and/or other speed values), and oneor more previous cycle values (e.g., D(t), D(t−1), D(t−2), and/or othercycle values). For example, plurality of neurons 202 may generate thecycle value based on a speed value (e.g., ω(t)) associated with motor206 at a particular time (e.g., t), and based on a current value (e.g.,i(t)) associated with motor 206 at the particular time (e.g., t).

In some examples, plurality of neurons 202 may generate the cycle valuebased on a previous speed value (e.g., ω(t−1)) associated with motor 206at a previous time (e.g., t−1) that occurred before the particular time(e.g., t) and/or a previous current value (e.g., i(t−1) associated withmotor 206 at the previous time (e.g., t−1). In some examples, pluralityof neurons 202 may generate the cycle value based on a second previousspeed value (e.g., ω(t−2)) associated with motor 206 at a previous time(e.g., t−2) that occurred before the particular time (e.g., t) andbefore the previous time (e.g., t−1) and/or a second previous currentvalue (e.g., i(t−2) associated with motor 206 at the second previoustime (e.g., t−2)).

In some examples, plurality of neurons 202 may generate the cycle valuebased on one or more of a first previous cycle value (e.g., D(t−1))associated with motor 206 at a previous time (e.g., t−1) that occurredbefore the particular time (e.g., t), a second previous cycle value(e.g., D(t−2)) associated with motor 206 at a second previous time(e.g., t−2) that occurred before the particular time (e.g., t) andbefore the previous time (e.g., t−1) and/or a third previous cycle value(e.g., D(t−3)) associated with motor 206 at a third previous time (e.g.,t−3) that occurred before the particular time (e.g., t), before thesecond previous time (e.g., t−1), and before the third previous time(e.g., t−2).

FIG. 3 is a block diagram illustrating an example system 300 configuredfor training a plurality of neurons 302, in accordance with one or moretechniques of this disclosure. Plurality of neurons 302, motor circuitry312, and motor 306 may be examples of plurality of neurons 102, motorcircuitry 112, and motor 106 of FIG. 1.

In accordance with the techniques of the disclosure, neural networkcircuitry may be configured to have, for each training vector of theplurality of training vectors, output the training cycle value to motorcircuitry 312 and to have determined a resulting speed value associatedwith motor 306 that occurs when the training cycle value has been outputto motor circuitry 312. For example, when plurality of neurons 302 ispre-trained, neural network circuitry (e.g., neural network circuitry110 of FIG. 1) may have stored a first previous cycle value (e.g.,D(t−1)), a previous current value (e.g., i(t−1)), and a previous speedvalue (e.g., ω(t−1)). In this example, the neural network circuitry mayhave determined a resulting speed value (e.g., ω(t)), for example, usingspeed circuitry 222 of FIG. 2.

In this example, system 300 may have generated a first training vectorto include the previous cycle value as a training cycle value, theprevious current value as a current value, the previous speed value as acurrent speed value, and the resulting speed value as a target speed. Inthe example of FIG. 3, system 300 may have generated the first trainingvector to include one or more of a second previous current value (e.g.,i(t−2)) or a second previous speed value (e.g., ω(t−2)). System 300 mayhave generated the first training vector to include one or more of athird previous current value (e.g., i(t−3)) or a third previous speedvalue (e.g., ω(t−3)). System 300 may have generated the first trainingvector to include one or more of a second previous cycle value (e.g.,D(t−2)), a third previous cycle value (e.g., D(t−3)), or a fourthprevious cycle value (e.g., D(t−4)).

System 300 may be configured to have generated the plurality of trainingvectors based on a range of target speed values for motor 306. Forexample, neural network circuitry may have generated the plurality oftraining vectors to help to ensure that motor 306 is within apredetermined range of speed values for motor 306. In this way, theneural network circuitry may be configured to have driven motor 306 tospeed values to prevent damage to motor 306 and/or to represent a rangeof expected speed values for a particular application of motor 306.

System 300 may be configured to have generated the plurality of trainingvectors based on a range of target current values for motor 306 thathave been determined based on a range of torque values. For example, theneural network circuitry may be configured to have converted a receivedrange of torque values to a range of current values using apredetermined factor (e.g., a torque constant). In this way, system 300may be configured to have driven motor 306 to prevent damage to motor306 and/or to represent a range of expected torque values for aparticular application of motor 306.

System 300 may be configured to have trained plurality of neurons 302 tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors. For example,system 300 may have trained plurality of neurons 302 using the firsttraining vector. In this example, system 300 may be configured to havegenerated a second training vector. For example, system 300 mayeffectively “shift” current values, speed values, and/or cycle valuesand repeat the process for generating the first training vector. Forinstance, system 300 may have used the first previous cycle value (e.g.,D(t−1)) for the first training vector as a second previous cycle value(e.g., D(t−2)) for the second training vector, may have used theprevious current value (e.g., i(t−1)) for the first training vector as asecond previous current value (e.g., i(t−2)) for the second trainingvector, may have used the previous speed value (e.g., ω(t−1)) for thefirst training vector as a second previous speed value (e.g., ω(t−2))for the second training vector, and so on. In this way, system 300 mayoperate with less memory storage consumption compared to systems thatbatch train neurons using multiple training vectors.

While the above example referred plurality of neurons 302 beingpre-configured, in some examples, plurality of neurons 302 may beuntrained and system 300 may train plurality of neurons 302 in realtime. For example, system 300 may be configured to generate a pluralityof training vectors and train plurality of neurons 302 to generate thecycle value to minimize the error between the cycle value and thetraining cycle value for each training vector of the plurality oftraining vectors.

In some examples, neural network circuitry may determine a previousspeed value (e.g., ω(t−1)) associated with motor 306 at a first previoustime (e.g., t−1), determine a previous current value (e.g., i(t−1))associated with motor 306 at the first previous time, determine aprevious cycle value (e.g., D(t−2)) for motor 306 at a second previoustime (e.g., t−2) that occurs before the first previous time (e.g., t−1),and output, at a current time (e.g., t) that occurs after the previoustime, the training cycle value (e.g., D(t−1)) to motor circuitry 312. Inthese examples, the neural network circuitry may determine a resultingspeed value (e.g., ω(t)) associated with motor 306 that occurs when thetraining cycle value (e.g., D(t−1)) has been output to motor circuitry312. In this example, the neural network circuitry is configured totrain plurality of neurons 302 to generate, based on the previous speedvalue (e.g., ω(t−1)), the previous current value (e.g., i(t−1)), theprevious cycle value (e.g., D(t−2)), and the resulting speed value e.g.,ω(t)), the cycle value to correspond to the training cycle value (e.g.,D(t−1)).

In the example of FIG. 3, system 300 may generate the training vector toinclude one or more of a second previous current value (e.g., i(t−2)) ora second previous speed value (e.g., ω(t−2)). System 300 may generatethe training vector to include one or more of a third previous currentvalue (e.g., i(t−3)) or a third previous speed value (e.g., ω(t−3)).System 300 may generate the training vector to include one or more of asecond previous cycle value (e.g., D(t−2)), a third previous cycle value(e.g., D(t−3)), or a fourth previous cycle value (e.g., D(t−4)). In someexamples, system 300 may generate the training vector to omit theprevious cycle value (e.g., D(t−2)).

System 300 may be configured to generate the plurality of trainingvectors based on a range of target speed values for motor 306. Forexample, system 300 may generate the plurality of training vectors tohelp to ensure that motor 306 is within a predetermined range of speedvalues for motor 306. In this way, system 300 may be configured to drivemotor 306 to speed values to prevent damage to motor 306 and/or torepresent a range of expected speed values for a particular applicationof motor 306.

System 300 may be configured to generate the plurality of trainingvectors based on a range of target current values for motor 306 thathave been determined based on a range of torque values. For example,system 300 may be configured to convert a received range of torquevalues to a range of current values using a predetermined factor (e.g.,a torque constant). In this way, system 300 may be configured to drivemotor 306 to prevent damage to motor 306 and/or to represent a range ofexpected torque values for a particular application of motor 306.

While the example of FIG. 3 illustrates three current values, four speedvalues, and three cycle values, in some examples plurality of neurons312 may use different values. For example, plurality of neurons 302 mayuse one, two, three, or more than four speed values. In some examples,plurality of neurons 302 may use one, two, or more than three currentvalues. Plurality of neurons 302 may use zero, one, two, or more thanthree cycle values. In some cases, plurality of neurons 302 may also useadditional information not shown to generate a cycle value.

FIG. 4 is a block diagram illustrating an example plurality of neurons,in accordance with one or more techniques of this disclosure. As shown,input layer 430 may include an input layer configured to receive atarget speed, one or more speed values (e.g., ω(t), ω(t−1), ω(t−2),and/or other speed values), one or more current values (e.g., i(t),i(t−1), i(t−2), and/or other current values), and one or more cyclevalues (e.g., D(t−1), D(t−2), D(t−3), and/or other cycle values). Insome examples, input layer 430 may include fewer inputs. For instance,input layer 430 may omit previous cycle values. In some instances, inputlayer 403 may include only a target speed, one speed value, and onecurrent value.

In the example of FIG. 4, hidden layer 432 includes seven neurons.However, in some examples, hidden layer 432 may include fewer neurons(e.g., less than seven neurons) or more neurons (e.g., more than sevenneurons). Each neuron of hidden layer 432 may be associated with aweight illustrated as b₁, b₂, . . . b₇. Each neuron of hidden layer 432may generate an output based on input layer 430. In this example, theoutput of each neuron of input layer 430 is multiplied by a respectiveweight to generate a weighted output value. Output layer 434 may outputa cycle value based on a summation of the weighted output values outputby each neuron of input layer 430. In this example, output layer 434includes one node (e.g., for a cycle value), however, in other examples,output layer 434 may include one or more additional nodes.

FIG. 5 is a block diagram illustrating an example setup for training aplurality of neurons, in accordance with one or more techniques of thisdisclosure. A first circuit board 546 may be an example of a combinationof plurality of neurons 102, motor circuitry 112, supply 101, and set ofswitching elements 114 of FIG. 1.

First circuit board 546 may be an example of motor control circuitryformed using an evaluation board including the system-on-chip with themicrocontroller, a set of switching elements (e.g., power MOSFETs) todrive, using neural network circuitry, drive motor 506. In the exampleof FIG. 5, the neural network may be realized in embedded softwarestored in memory of the microcontroller of first circuit board 546. Theembedded software for realizing the neural network may include a libraryto train and execute a plurality of neurons. Drive motor 506 may be anexample of motor 106 of FIG. 1. Second circuit board 544 with resistorboard 542 may be configured to drive load motor 540 (e.g., a DC motor)to act as a variable mechanical load.

In accordance with the techniques of the disclosure, a plurality ofneurons of first circuit board 546 may be configured to be trained togenerate a cycle value to minimize an error between the cycle value anda training cycle value for each training vector of a plurality oftraining vectors. First circuit board 546 may include a PWM generatorthat converts the cycle value into a pulse-width modulated duty cycle.The set of switching elements of first circuit board 546 may control(e.g., drive) drive motor 506 using the pulse-width modulated dutycycle.

Rather than training the plurality of neurons of first circuit board 546at a constant load, second circuit board 544 may be configured to driveload motor 540, which includes a rotor mechanically coupled to a rotorof drive motor 506, to apply a variable mechanical load to drive motor506. For example, second circuit board 544 may select (e.g., switch-in)a first set of resistors of resistor board 542 corresponding to a firstpredetermined mechanical load. In this example, first circuit board 546may generate a first training vector when second circuit board 544selects the first set of resistors for the first predeterminedmechanical load. In this example, second circuit board 544 may select(e.g., switch-in) a second set of resistors of resistor board 542corresponding to a second predetermined mechanical load that is greaterthan or less than the first predetermined mechanical load. In thisexample, first circuit board 546 may generate a second training vectorwhen second circuit board 544 selects the second set of resistors forthe second predetermined mechanical load. In this way, the plurality ofneurons may be trained to account for different loading at drive motor506 when drive motor 506 is in-service (e.g., mechanically coupled to avarying load that is not preconfigured).

FIG. 6 is a conceptual diagram illustrating an example resistor board642 for training a plurality of neurons, in accordance with one or moretechniques of this disclosure. Resistor board 642 may be an example ofresistor board 542 of FIG. 5. As shown, resistor board 642 includesswitching elements 60A-660G (collectively, “switching elements 660”).Second circuit board 544 of FIG. 5 is discussed with respect to FIG. 6for example purposes only.

Second circuit board 544 may switch-in a first set of switching elements660 to generate a first predetermined mechanical load. For example,second circuit board 544 may switch-in switching elements 660A-660C andswitch-out switching elements 660D-660G to generate a first resistancevalue. In this example, second circuit board 544 may drive load motor540 using the first resistance value to provide a first predeterminedmechanical load. Second circuit board 544 may switch-in switchingelement 660A and switch-out switching elements 660B-660G to generate asecond resistance value. In this example, second circuit board 544 maydrive load motor 540 using the second resistance value to provide asecond predetermined mechanical load. In this way, resistor board 642may help to provide resistance values to drive load motor 540 with apreconfigured variable mechanical load.

FIG. 7 is a block diagram illustrating an example first systemconfigured for driving a motor using a first plurality of neurons and asecond plurality of neurons, in accordance with one or more techniquesof this disclosure. Neural network circuitry 710, motor circuitry 712,set of switching elements 714, supply 701, and motor 706 may be examplesof neural network circuitry 110, motor circuitry 112, set of switchingelements 114, supply 101, and motor 106 of FIG. 1.

System 700 of FIG. 7 may be an adaptive system and may include twoneural networks (e.g., first plurality of neurons 702A and secondplurality of neurons 702B). First plurality of neurons 702A may betrained and introduced in the motor regulation loop. First plurality ofneurons 702A may receive input signals (e.g., one or more speed values,one or more current values, and/or other values) from motor 706 andgenerate a first cycle value. Motor circuitry 712 may generate, based onthe first cycle value, a digital modulation signal (e.g., a PWM dutycycle) to control motor 706. Second plurality of neurons 702A maycomprise a copy of first plurality of neurons 702A.

As described in further details below, first plurality of neurons 702Amay receive a target speed as an input. In some examples, firstplurality of neurons 702A may receive values (e.g., a current value, aspeed value, and/or another value) and historical values (e.g., one ormore previous current values, one or more previous speed values, one ormore previous cycle values, and/or another value). Second plurality ofneurons 702A may receive the same values (e.g., one or more currentvalues, one or more speed values, one or more cycle values, and/oranother value) delayed. However, second plurality of neurons 702A mayreceive the resulting speed (e.g., based on motor BEMF voltage) insteadof the target speed.

Neural network circuitry 710 may use a delta (e.g., a difference value)of the output of first plurality of neurons 702A and second plurality ofneurons 702B as an error measure which may be used to adapt secondplurality of neurons 702B which is not in the regulation loop. When theerror is sufficiently low, second plurality of neurons 702B is exchangedagainst first plurality of neurons 702A and the copy of second pluralityof neurons 702A may be again adapted in the same way as before. Forinstance, neural network circuitry 710 may set second plurality ofneurons 702B to become first plurality of neurons 702A and set firstplurality of neurons 702A to become second plurality of neurons 702B.With this implementation system 700 may continuously adjust and assessthe error. Neural network circuitry 710 may assess the error in thebackground of the regulation. For example, neural network circuitry 710may assess the error without interrupting the regulation of motor 706 byfirst plurality of neurons 702A. In this way, neural network circuitry710 may optimize and adapt based on error (e.g., a difference) betweenthe actual setup and the ideal setup, which may improve an accuracy ofsystem 700 compared to motor controllers using programmed motorcontrollers and/or statically implemented neural networks.

Neural network circuitry 710 may include first plurality of neurons 702Aand second plurality of neurons 702B. First plurality of neurons 702Amay be an example of plurality of neurons 102 of FIG. 1. Secondplurality of neurons 702B may similar to first plurality of neurons702B. For example, first plurality of neurons 702A and second pluralityof neurons 702B may include an input layer configured to receive one ormore of a target speed, one or more speed values, one or more currentvalues, or one or more previous cycle values. First plurality of neurons702A and second plurality of neurons 702B may include a hidden layerwith less than seven neurons, seven neurons, more than seven neurons, orother numbers of neurons.

First plurality of neurons 702A may be configured to generate a firstcycle value based on a target speed. First plurality of neurons 702A maybe configured to generate the first cycle value further based onadditional values, for example, one or more speed values, one or morecurrent values, one or more cycle values, or other values.

Motor circuitry 712 may be configured to control, based on the firstcycle value, set of switching elements 714 to drive motor 706. Forexample, motor circuitry 712 may include circuitry configured togenerate, based on the cycle value (e.g., a duty cycle value), a digitalmodulated signal. Motor circuitry 712 may be configured to drive, basedon the digital modulated signal, set of switching elements 714 tooperate in at least a first switching state and a second switchingstate. During the first switching state, set of switching elements 714may electrically couple a first terminal of motor 706 and a first supplyterminal (e.g., a positive terminal or a negative terminal) of supply701 and electrically couples a second terminal of the motor and a secondsupply terminal (e.g., a negative terminal or a positive terminal) ofsupply 701. During the second switching state, set of switching elements714 may electrically couple the second terminal of the motor and thefirst supply terminal (e.g., a positive terminal or a negative terminal)of supply 701 and may electrically couple the first terminal of themotor and the second supply terminal (e.g., a negative terminal or apositive terminal) of supply 701.

Some systems may train neurons at manufacturing and/or when initiallyplacing a motor controller in-service. However, various parameters(e.g., mechanical, electrical, and/or other parameters) of the systemmay change while the system is in service. Such changes in parametersmay reduce an accuracy of the neurons over time.

In accordance with the techniques of the disclosure, neural networkcircuitry 710 may not be static (e.g., maintain an initial training ofneurons). Instead, neural network circuitry 710 may be configured toadapt and “learn on the fly” while system 700 is in-service. When system700 changes because of mechanical changes or changes of elements, neuralnetwork circuitry 710 may be configured to learn from the behavior ofthe new setup and while neural network circuitry 710 controls, forexample, a speed or a torque at motor 706. For example, neural networkcircuitry 710 may be configured to compare an actual setup (e.g.,corresponding to system 700 in-service) with an ideal setup (e.g.,corresponding to system 700 when manufactured and/or when initiallyplaced in-service). Techniques described herein may configure neuralnetwork circuitry 710 to optimize and adapt based on error (e.g., adifference) between the actual setup and the ideal setup, which mayimprove an accuracy of system 700 compared to motor controllers usingprogrammed motor controllers and/or statically implemented neuralnetworks.

Second plurality of neurons 702B may be configured to train secondplurality of neurons 702B to generate, based on a resulting speed value(e.g., ω(t)) for motor 706 that occurs when motor circuitry 712 hascontrolled set of switching elements 714 to drive motor 706 based on thefirst cycle value, a second cycle value to minimize a difference betweenthe second cycle value and the first cycle value. For example, neuralnetwork circuitry 712 may determine one or more of a previous speedvalue (e.g., ω(t−1)) associated with motor 706 at a first previous time(e.g., t−1), a previous current value (e.g., i(t−1)) associated withmotor 306 at the first previous time, a previous cycle value (e.g.,D(t−2)) for motor 306 at a second previous time (e.g., t−2) that occursbefore the first previous time (e.g., t−1). In this example, firstplurality of neurons 702A may output, at a current time (e.g., t) thatoccurs after the previous time, the first cycle value (e.g., D(t−1)) tomotor circuitry 712 to drive motor 706. In this example, neural networkcircuitry 710 may determine the resulting speed value (e.g., ω(t))associated with motor 706 that occurs when the first cycle value (e.g.,D(t−1)) has been output to motor circuitry 712 to drive motor 706. Inthis example, neural network circuitry 712 may be configured to trainsecond plurality of neurons 702B to generate, based on the resultingspeed value e.g., ω(t)), the second cycle value to minimize a differencebetween the second cycle value (e.g., the cycle value output by secondplurality of neurons 702B) and the first cycle value (e.g., D(t−1)).

In this way, second plurality of neurons 702B may train in-real time andwhile system 700 is operating in-service in order to help account formechanical changes, electrical changes, and/or other changes in system700. For example, neural network circuitry 710 may adapt and learn howto regulate optimally when motor 706 is exchanged, when one or more ballrings of motor 706 dry out, when mechanical wear out takes place atmotor 706, and/or in response to other changes in system 700. Neuralnetwork circuitry 710 may adapt and learn how to regulate optimally inresponse to changes in system 700 autonomously (e.g., without human userinteraction and/or without software adjustment).

FIG. 8 is a block diagram illustrating an example system 800 configuredfor training a second plurality of neurons 802B, in accordance with oneor more techniques of this disclosure. First plurality of neurons 802A,motor circuitry 812, and motor 806 may be examples of plurality ofneurons 102, motor circuitry 112, and motor 106 of FIG. 1. Maximum speeddelta circuitry 803, current circuitry 820, and speed circuitry 822 maybe examples of maximum speed delta circuitry 203, current circuitry 220,and speed circuitry 222 of FIG. 2. Second plurality of neurons 802B maybe an example of second plurality of neurons 702B of FIG. 7. Firstplurality of neurons 802A and second plurality of neurons 802B may becollectively referred to herein as neural network circuitry 802, whichmay include circuitry in addition to first plurality of neurons 802A andsecond plurality of neurons 802B.

While first plurality of neurons 802A may generate the first cycle valuein real-time, the proceeding examples refer to first plurality ofneurons 802A as generating the first cycle value at time ‘t−1’ andrefers to the resulting speed value (e.g., ω(t)) as occurring at time‘t’ for ease of description. Speed circuitry 822 may measure aback-electromagnetic force voltage at motor 806 when motor circuitry 812has controlled set of switching elements 814 to drive motor 806 based onthe first cycle value and determine the resulting speed value (e.g.,ω(t)) for motor 806 based on the back-electromagnetic force voltage.

First plurality of neurons 802A may generate the first cycle value(e.g., D(t−1)) based on a target speed output by maximum speed deltacircuitry 803. In some examples, first plurality of neurons 802A maygenerate the first cycle value based on the target speed and furtherbased on one or more of a first speed value (e.g., ω(t−1)) for motor 806at a first time (e.g., t−1) that occurs before motor circuitry 812 hascontrolled, based on the first cycle value, set of switching elements814, a first current value (e.g., i(t−1)) for motor 806 at the firsttime (e.g., t−1), or a first previous cycle value (e.g., D(t−2)) used bymotor circuitry 812 to control set of switching elements 814 at a secondtime (e.g., t−2) that occurred before the first time (e.g., t−1). Insome examples, first plurality of neurons 802A may generate the firstcycle value based on one or more of a second speed value (e.g., ω(t−2))for motor 806 at a second time (e.g., t−2) that occurred before thefirst time (e.g., t−1), a second current value (e.g., i(t−2)) for motor806 at the second time (e.g., t−2), or a second previous cycle value(e.g., D(t−3)) used by motor circuitry 812 to control set of switchingelements 814 at a third time (e.g., t−3) that occurred before the secondtime (e.g., t−2).

In some examples, neural network circuitry 802 (e.g., first plurality ofneurons 802A and second plurality of neurons 802B) may be configured totrain second plurality of neurons 802B using the first cycle value. Forexample, neural network circuitry 802 may be configured to train secondplurality of neurons 802B to generate, based on the resulting speedvalue (e.g., ω(t)), the second cycle value (e.g., D′(t−1)) to minimize adifference (e.g., error) between the second cycle value (e.g., the cyclevalue output by second plurality of neurons 802B) and the first cyclevalue (e.g., D(t−1)). The difference (e.g., error) between the secondcycle value (e.g., the cycle value output by second plurality of neurons802B) and the first cycle value (e.g., D(t−1)) is input to neuralnetwork circuitry 802 and used, by neural network circuitry 802, toadapt the second plurality of neurons 802B.

In some examples, second plurality of neurons 802B may generate thesecond cycle value based on the resulting speed value (e.g., ω(t)) andbased further on one or more of: one or more current values (e.g.,i(t−1), i(t−2), i(t−3), and/or other current values), one or more speedvalues (e.g., ω(t−1), ω(t−2), ω(t−3), and/or other speed values), andone or more previous cycle values (e.g., D(t−2), D(t−3), D(t−4), and/orother cycle values).

For example, second plurality of neurons 802B may generate the secondcycle value based on the resulting speed value (e.g., ω(t)) and furtherbased on one or more of a first speed value (e.g., ω(t−1)) for motor 806at a first time (e.g., t−1) that occurs before motor circuitry 812 hascontrolled, based on the first cycle value, set of switching elements814, a first current value (e.g., i(t−1)) for motor 806 at the firsttime (e.g., t−1), or a first previous cycle value (e.g., D(t−2)) used bymotor circuitry 812 to control set of switching elements 814 at a secondtime (e.g., t−2) that occurred before the first time (e.g., t−1). Insome examples, second plurality of neurons 802B may generate the secondcycle value based on one or more of a second speed value (e.g., ω(t−2))for motor 806 at a second time (e.g., t−2) that occurred before thefirst time (e.g., t−1), a second current value (e.g., i(t−2)) for motor806 at the second time (e.g., t−2), or a second previous cycle value(e.g., D(t−3)) used by motor circuitry 812 to control set of switchingelements 814 at a third time (e.g., t−3) that occurred before the secondtime (e.g., t−2).

Neural network circuitry 802 may be configured to adapt to changes insystem 800. For example, in response to determining that the differencebetween the second cycle value and the first cycle value is less than anerror threshold (e.g., a preconfigured threshold, a determinedthreshold, and/or another error threshold), the neural network circuitryis configured to copy second plurality of neurons 802B into firstplurality of neurons 802A such that the first plurality of neuronsgenerates the first cycle value to correspond to the second cycle valuegenerated by second plurality of neurons 802B when the resulting speedvalue corresponds to the target speed. For example, the neural networkcircuitry is configured to replace first values of weights of a hiddenlayer (e.g., b₁, b₂, . . . , b₇ of FIG. 4) of first plurality of neurons802A with second values of corresponding weights of the hidden layer ofsecond plurality of neurons 802B.

FIG. 9 is a flow diagram for training a second plurality of neurons, inaccordance with one or more techniques of this disclosure. The exampleoperations of FIG. 9 are described below within the context of FIGS. 1-8for example purposes only. Neural network circuitry 710 may train firstplurality of neurons 702A (902). For example, neural network circuitry710 may train first plurality of neurons 702A in accordance withtechniques described in FIG. 3 and/or FIG. 5. For instance, neuralnetwork circuitry 710 may train first plurality of neurons 702A when apredetermined variable mechanical load is applied to motor 706. In someexamples, the predetermined variable mechanical load may be applied by aload motor (e.g., load motor 540) using a resistor board (e.g., resistorboard 542). For instance, each different combination of resistorsswitched-in may correspond to a different predetermined variablemechanical load. In some examples, second plurality of neurons 702B maybe configured to refrain from training when the predetermined variablemechanical load is applied to motor 706.

Neural network circuitry 710 may copy the first plurality of neurons702A to second plurality of neurons 702B (904). For example, neuralnetwork circuitry 710 may be configured to replace first values ofweights of a hidden layer (e.g., b₁, b₂, . . . , b₇ of FIG. 4) of firstplurality of neurons 702A with second values of corresponding weights ofthe hidden layer of second plurality of neurons 702B.

Neural network circuitry 710 executes regulation (906). For example,first plurality of neurons 702A may generate a first cycle value basedon a target speed. First plurality of neurons 702A may be configured togenerate the first cycle value further based on additional values, forexample, one or more speed values, one or more current values, one ormore cycle values, or other values.

Neural network circuitry 710 may calculate an error between firstplurality of neurons 702A and second plurality of neurons 702B (908).For example, neural network circuitry 710 may calculate a differencebetween the first duty cycle generated by first plurality of neurons702A at a previous time (e.g., t−1) and the second duty cycle generatedby second plurality of neurons 702B at a particular time (e.g., t).

Neural network circuitry 710 may adapt second plurality of neurons 702Bbased on the error (910). For example, second plurality of neurons 702Bmay be configured to train, when the predetermined variable mechanicalload is not applied to the motor, second plurality of neurons 702B togenerate, based on a resulting speed value for motor 706 that occurswhen motor circuitry 712 has controlled set of switching elements 714 todrive motor 706 based on the first cycle value, a second cycle value tominimize a difference between the second cycle value and the first cyclevalue. For instance, second plurality of neurons 702B may be configuredto train during when first plurality of neurons 702A generates a firstcycle value based on a target speed and when motor 706 is coupled to avarying and unknown load and not coupled to a load motor.

Neural network circuitry 710 may determine whether a difference betweenthe second cycle value and the first cycle value is less than an errorthreshold (e.g., a preconfigured threshold, a determined threshold,and/or another error threshold) (912). In response to determining thatthe difference between the second cycle value and the first cycle valueis not less than the error threshold (“No” of 912), neural networkcircuitry 710 may proceed to step 908. In response, however, todetermining that the difference between the second cycle value and thefirst cycle value is less than an error threshold (“Yes” of 912), neuralnetwork circuitry 710 may copy second plurality of neurons 702B to firstplurality of neurons 702A and proceed to step 904 (914). For example,neural network circuitry 710 may copy second plurality of neurons 702Bto first plurality of neurons 702A such that the first plurality ofneurons generates the first cycle value to correspond to the secondcycle value generated by second plurality of neurons 702B when theresulting speed value corresponds to the target speed. For instance,neural network circuitry 710 may be configured to replace first valuesof weights of a hidden layer (e.g., b₁, b₂, . . . , b₇ of FIG. 4) offirst plurality of neurons 702A with second values of correspondingweights of the hidden layer of second plurality of neurons 702B.

FIG. 10 is graph illustrating a mean squared error during training, inaccordance with one or more techniques of this disclosure. FIG. 10 isdiscussed with reference to FIGS. 1-9 for example purposes only. Theabscissa axis of FIG. 10 is time and the ordinate axis of FIG. 10 is amean squared error 1002 of target speed and a resulting speed value atmotor 106 during training of plurality of neurons 102. As shown, neuralnetwork circuitry 110 is configured to train plurality of neurons 102 tosufficiently reduce the mean squared error during training.

FIG. 11 is a graph illustrating BEMF voltage tracking, in accordancewith one or more techniques of this disclosure. FIG. 11 is discussedwith reference to FIGS. 1-10 for example purposes only. The abscissaaxis of FIG. 11 is time in seconds and the ordinate axis of FIG. 11 is afirst BEMF voltage 1102 of a target speed and a second BEMF voltage 1104of a resulting speed value at motor 106. As shown, neural networkcircuitry 110 is configured to closely regulate a rotor speed at motor106 to a target speed.

FIG. 12 is a graph illustrating BEMF voltage tracking during loadchanges, in accordance with one or more techniques of this disclosure.FIG. 12 is discussed with reference to FIGS. 1-11 for example purposesonly. The abscissa axis of FIG. 12 is time in seconds and the ordinateaxis of FIG. 12 is a first BEMF voltage 1202 of a target speed and asecond BEMF voltage 1204 of a resulting speed value at motor 106. Asshown, neural network circuitry 710 is configured to closely regulate,with first plurality of neurons 702A, a rotor speed at motor 706 to atarget speed until a load change occurs at about 8 seconds. At 8seconds, neural network circuitry 710 may continuously train and adaptsecond plurality of neurons 702B, which is copied into first pluralityof neurons 702A to more closely regulate second BEMF voltage 1204 of aresulting speed value at motor 106 to correspond to first BEMF voltage1202 of the target speed. At about 13 seconds, another load changeoccurs and neural network circuitry 710 may continuously train and adaptsecond plurality of neurons 702B, which is copied into first pluralityof neurons 702A to more closely regulate second BEMF voltage 1204 of aresulting speed value at motor 106 to correspond to first BEMF voltage1202 of the target speed. In this way, neural network circuitry 710 mayoptimize and adapt to account for changes in system 700 (e.g., motor706), which may improve an accuracy of system 700 compared to motorcontrollers using programmed motor controllers and/or staticallyimplemented neural networks.

FIG. 13 is a flow diagram for driving a motor using neural networkcircuitry, in accordance with one or more techniques of this disclosure.FIG. 13 is discussed with reference to FIGS. 1-12 for example purposesonly. Plurality of neurons 102 may generate a cycle value based on atarget speed, based on a speed value associated with the motor at aparticular time, and based on a current value associated with the motorat the particular time (1302). In some examples, plurality of neurons102 may be configured to be trained to generate the cycle value tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors.

In some examples, system 100 may be configured to have generated theplurality of training vectors. For example, system 100 may, withouthuman interaction, have selected different starting motor speeds,different loads at motor 106, different cycle values, and/or otherparameters to have generated training vectors of the set of trainingvectors. In some examples, system 100 may, without human interaction, beconfigured to select different starting motor speeds, different loads atmotor 106, different cycle values, and/or other parameters to generatetraining vectors of the set of training vectors. Motor circuitry 212 maycontrol, based on the cycle value, set of switching elements 114 todrive motor 106 (1304).

FIG. 14 is a flow diagram for driving a motor using a first plurality ofneurons and a second plurality of neurons, in accordance with one ormore techniques of this disclosure. FIG. 14 is discussed with referenceto FIGS. 1-13 for example purposes only. First plurality of neurons 702Amay generate a first cycle value based on a target speed (1402). In someexamples, first plurality of neurons 702A may be configured to generatethe first cycle value further based on additional values, for example,one or more speed values, one or more current values, one or more cyclevalues, or other values.

Motor circuitry 712 may control, based on the first cycle value, set ofswitching elements 714 to drive motor 706 (1404). Neural networkcircuitry 710 may train second plurality of the neurons 702B togenerate, based on a resulting speed value for motor 706 that occurswhen motor circuitry 712 has controlled set of switching elements 714 todrive motor 706 based on the first cycle value, a second cycle value tominimize a difference between the second cycle value and the first cyclevalue (1406).

The following examples may illustrate one or more aspects of thedisclosure.

Example 1. An apparatus for driving a motor, the apparatus comprising: aplurality of neurons of neural network circuitry configured to generatea cycle value based on a target speed, based on a speed value associatedwith the motor at a particular time, and based on a current valueassociated with the motor at the particular time, wherein the pluralityof neurons is configured to be trained to generate the cycle value tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors and wherein theapparatus is configured to have generated the plurality of trainingvectors; and motor circuitry configured to control, based on the cyclevalue, a set of switching elements to drive the motor.

Example 2. The apparatus of example 1, wherein the apparatus isconfigured to have generated the plurality of training vectors based onat least one of a range of target speed values for the motor or a rangeof target current values for the motor that have been determined basedon a range of torque values.

Example 3. The apparatus of any combination of examples 1-2, wherein theneural network circuitry is configured to, for each training vector ofthe plurality of training vectors, have output the training cycle valueto the motor circuitry and to have determined a resulting speed valueassociated with the motor that occurs when the training cycle value hasbeen output to the motor circuitry.

Example 4. The apparatus of any combination of examples 1-3, wherein themotor circuitry is configured to: measure a back-electromagnetic forcevoltage at the motor and at the particular time; and determine the speedvalue associated with the motor at the particular time based on theback-electromagnetic force voltage.

Example 5. The apparatus of any combination of examples 1-4, wherein, togenerate the cycle value, the neural network is configured to generatethe cycle value further based on a previous speed value associated withthe motor at a previous time that occurred before the particular timeand a previous current value associated with the motor at the previoustime.

Example 6. The apparatus of any combination of examples 1-5, wherein themotor circuitry is configured to: receive a reference speed; anddetermine the target speed based on the reference speed, the speed valueassociated with the motor at the particular time, and one or moreprevious speed values associated with the motor at one or more previoustimes that occur before the particular time.

Example 7. The apparatus of any combination of examples 1-6, wherein theapparatus is configured to: generate the plurality of training vectors;and train the plurality of neurons to generate the cycle value tominimize the error between the cycle value and the training cycle valuefor each training vector of the plurality of training vectors.

Example 8. The apparatus of example 7, wherein, to generate theplurality of training vectors, the apparatus is configured to, for eachtraining vector of the plurality of training vectors: determine aprevious speed value associated with the motor at a first previous time;determine a previous current value associated with the motor at thefirst previous time; determine a previous cycle value for the motor at asecond previous time that occurs before the first previous time; output,at a current time that occurs after the previous time, the trainingcycle value to the motor circuitry; and determine a resulting speedvalue associated with the motor that occurs when the training cyclevalue has been output to the motor circuitry, wherein, to train theplurality of neurons, the neural network circuitry, is configured totrain the plurality of neurons to generate, based on the previous speedvalue, the previous current value, the previous duty cycle, and theresulting speed value, the cycle value to correspond to the trainingduty cycle.

Example 9. The apparatus of example 8, wherein, to generate theplurality of training vectors, the apparatus is configured to limitspeed at the motor to a range of operating speed values for the motorand limit current at the motor to a range of operating current valuesfor the motor.

Example 10. The apparatus of any combination of examples 8-9, wherein,to generate the plurality of training vectors, the apparatus isconfigured to, for each training vector of the plurality of trainingvector: determine a second previous speed value associated with themotor at the second previous time that occurred before the previoustime; determine a second previous current value associated with themotor at the second previous time; and determine a second previous cyclevalue for the motor at a third previous time that occurred before thesecond previous time, wherein, to train the plurality of neurons, theneural network circuitry, is configured to train the plurality ofneurons to generate, based further on the second previous speed value,the second previous current value, and the second previous duty cycle,the cycle value to correspond to the training duty cycle.

Example 11. The apparatus of any combination of examples 7-10, wherein avariable mechanical load is applied to the motor when the neural networktrains the plurality of neurons.

Example 12. The apparatus of any combination of examples 11, wherein aload motor mechanically coupled to the motor is configured to apply thevariable mechanical load to the motor when the neural network trains theplurality of neurons.

Example 13. The apparatus of any combination of examples 1-12, wherein,to control, based on the cycle value, the set of switching elements todrive the motor, the motor circuitry is configured to: generate, basedon the cycle value, a digital modulated signal; and drive, based on thedigital modulated signal, a set of switching elements to operate in atleast a first switching state and a second switching state, wherein,during the first switching state, the set of switching elementselectrically couples a first terminal of the motor and a first supplyterminal of a supply and electrically couples a second terminal of themotor and a second supply terminal of the supply and wherein, during thesecond switching state, the set of switching elements electricallycouples the second terminal of the motor and the first supply terminaland electrically couples the second terminal of the motor and the firstsupply terminal.

Example 14. The apparatus of any combination of examples 1-13, whereinthe motor comprises a DC brush less or a DC-excited motor.

Example 15. A method for driving a motor, the method comprising:generating, by a plurality of neurons of neural network circuitry of anapparatus for driving the motor, a cycle value based on a target speed,based on a speed value associated with the motor at a particular time,and based on a current value associated with the motor at the particulartime, wherein the plurality of neurons is configured to be trained togenerate the cycle value to minimize an error between the cycle valueand a training cycle value for each training vector of a plurality oftraining vectors and wherein the apparatus is configured to havegenerated the plurality of training vectors; and controlling, by motorcircuitry and based on the cycle value, a set of switching elements todrive the motor.

Example 16. The method of example 15, wherein the apparatus isconfigured to have generated the plurality of training vectors based onat least one of a range of target speed values for the motor or a rangeof target current values for the motor that have been determined basedon a range of torque values.

Example 17. The method of any combination of examples 15-16, wherein theneural network circuitry is configured to, for each training vector ofthe plurality of training vectors, have output the training cycle valueto the motor circuitry and to have determined a resulting speed valueassociated with the motor that occurs when the training cycle value hasbeen output to the motor circuitry.

Example 18. The method of any combination of examples 15-17, comprising:measuring, by speed circuitry, a back-electromagnetic force voltage atthe motor and at the particular time; and determining, by the speedcircuitry, the speed value associated with the motor at the particulartime based on the back-electromagnetic force voltage.

Example 19. The method of any combination of examples 15-18, whereingenerating the cycle value comprises generating the cycle value furtherbased on a previous speed value associated with the motor at a previoustime that occurred before the particular time and a previous currentvalue associated with the motor at the previous time.

Example 20. An apparatus for driving a motor, the apparatus comprising:a set of switching elements; a plurality of neurons of neural networkcircuitry configured to generate a cycle value based on a target speed,based on a speed value associated with the motor at a particular time,and based on a current value associated with the motor at the particulartime, wherein the plurality of neurons is configured to be trained togenerate the cycle value to minimize an error between the cycle valueand a training cycle value for each training vector of a plurality oftraining vectors and wherein the apparatus is configured to havegenerated the plurality of training vectors; and motor circuitryconfigured to control, based on the cycle value, the set of switchingelements to drive the motor.

Various aspects have been described in this disclosure. These and otheraspects are within the scope of the following claims.

The invention claimed is:
 1. An apparatus for driving a motor, theapparatus comprising: a plurality of neurons of neural network circuitryimplemented using one or more processors and configured to generate acycle value based on a target speed, based on a speed value associatedwith the motor at a particular time, and based on a current valueassociated with the motor at the particular time, wherein the pluralityof neurons is configured to be trained to generate the cycle value tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors and wherein theapparatus is configured to have generated the plurality of trainingvectors; and motor circuitry configured to control, based on the cyclevalue, a set of switching elements to drive the motor, wherein the motorcomprises a DC brushless or a DC-excited motor.
 2. The apparatus ofclaim 1, wherein the apparatus is configured to have generated theplurality of training vectors based on at least one of a range of targetspeed values for the motor or a range of target current values for themotor that have been determined based on a range of torque values. 3.The apparatus of claim 1, wherein the neural network circuitry isconfigured to, for each training vector of the plurality of trainingvectors, have output the training cycle value to the motor circuitry andto have determined a resulting speed value associated with the motorthat occurs when the training cycle value has been output to the motorcircuitry.
 4. An apparatus for driving a motor, the apparatuscomprising: a plurality of neurons of neural network circuitryimplemented using one or more processors and configured to generate acycle value based on a target speed, based on a speed value associatedwith the motor at a particular time, and based on a current valueassociated with the motor at the particular time, wherein the pluralityof neurons is configured to be trained to generate the cycle value tominimize an error between the cycle value and a training cycle value foreach training vector of a plurality of training vectors and wherein theapparatus is configured to have generated the plurality of trainingvectors; and motor circuitry configured to: control, based on the cyclevalue, a set of switching elements to drive the motor; measure aback-electromagnetic force voltage at the motor and at the particulartime; and determine the speed value associated with the motor at theparticular time based on the back-electromagnetic force voltage.
 5. Theapparatus of claim 1, wherein, to generate the cycle value, the neuralnetwork is configured to generate the cycle value further based on aprevious speed value associated with the motor at a previous time thatoccurred before the particular time and a previous current valueassociated with the motor at the previous time.
 6. An apparatus fordriving a motor, the apparatus comprising: a plurality of neurons ofneural network circuitry implemented using one or more processors andconfigured to generate a cycle value based on a target speed, based on aspeed value associated with the motor at a particular time, and based ona current value associated with the motor at the particular time,wherein the plurality of neurons is configured to be trained to generatethe cycle value to minimize an error between the cycle value and atraining cycle value for each training vector of a plurality of trainingvectors and wherein the apparatus is configured to have generated theplurality of training vectors; and motor circuitry configured to:control, based on the cycle value, a set of switching elements to drivethe motor; receive a reference speed; and determine the target speedbased on the reference speed, the speed value associated with the motorat the particular time, and one or more previous speed values associatedwith the motor at one or more previous times that occur before theparticular time.
 7. The apparatus of claim 1, wherein the apparatus isconfigured to: generate the plurality of training vectors; and train theplurality of neurons to generate the cycle value to minimize the errorbetween the cycle value and the training cycle value for each trainingvector of the plurality of training vectors.
 8. The apparatus of claim7, wherein, to generate the plurality of training vectors, the apparatusis configured to, for each training vector of the plurality of trainingvectors: determine a previous speed value associated with the motor at afirst previous time; determine a previous current value associated withthe motor at the first previous time; determine a previous cycle valuefor the motor at a second previous time that occurs before the firstprevious time; output, at a current time that occurs after the previoustime, the training cycle value to the motor circuitry; and determine aresulting speed value associated with the motor that occurs when thetraining cycle value has been output to the motor circuitry, wherein, totrain the plurality of neurons, the neural network circuitry, isconfigured to train the plurality of neurons to generate, based on theprevious speed value, the previous current value, the previous dutycycle, and the resulting speed value, the cycle value to correspond tothe training cycle.
 9. The apparatus of claim 8, wherein, to generatethe plurality of training vectors, the apparatus is configured to limitspeed at the motor to a range of operating speed values for the motorand limit current at the motor to a range of operating current valuesfor the motor.
 10. The apparatus of claim 8, wherein, to generate theplurality of training vectors, the apparatus is configured to, for eachtraining vector of the plurality of training vector: determine a secondprevious speed value associated with the motor at the second previoustime that occurred before the previous time; determine a second previouscurrent value associated with the motor at the second previous time; anddetermine a second previous cycle value for the motor at a thirdprevious time that occurred before the second previous time, wherein, totrain the plurality of neurons, the neural network circuitry, isconfigured to train the plurality of neurons to generate, based furtheron the second previous speed value, the second previous current value,and the second previous duty cycle, the cycle value to correspond to thetraining cycle.
 11. The apparatus of claim 7, wherein a variablemechanical load is applied to the motor when the neural network trainsthe plurality of neurons.
 12. The apparatus of claim 11, wherein a loadmotor mechanically coupled to the motor is configured to apply thevariable mechanical load to the motor when the neural network trains theplurality of neurons.
 13. The apparatus of claim 1, wherein, to control,based on the cycle value, the set of switching elements to drive themotor, the motor circuitry is configured to: generate, based on thecycle value, a digital modulated signal; and drive, based on the digitalmodulated signal, a set of switching elements to operate in at least afirst switching state and a second switching state, wherein, during thefirst switching state, the set of switching elements electricallycouples a first terminal of the motor and a first supply terminal of asupply and electrically couples a second terminal of the motor and asecond supply terminal of the supply and wherein, during the secondswitching state, the set of switching elements electrically couples thesecond terminal of the motor and the first supply terminal andelectrically couples the second terminal of the motor and the firstsupply terminal.
 14. A method for driving a motor, the methodcomprising: generating, by a plurality of neurons of neural networkcircuitry implemented using one or more processors and of an apparatusfor driving the motor, a cycle value based on a target speed, based on aspeed value associated with the motor at a particular time, and based ona current value associated with the motor at the particular time,wherein the plurality of neurons is configured to be trained to generatethe cycle value to minimize an error between the cycle value and atraining cycle value for each training vector of a plurality of trainingvectors and wherein the apparatus is configured to have generated theplurality of training vectors; and controlling, by motor circuitry andbased on the cycle value, a set of switching elements to drive themotor, wherein the motor comprises a DC brushless or a DC-excited motor.15. The method of claim 14, wherein the apparatus is configured to havegenerated the plurality of training vectors based on at least one of arange of target speed values for the motor or a range of target currentvalues for the motor that have been determined based on a range oftorque values.
 16. The method of claim 14, wherein the neural networkcircuitry is configured to, for each training vector of the plurality oftraining vectors, have output the training cycle value to the motorcircuitry and to have determined a resulting speed value associated withthe motor that occurs when the training cycle value has been output tothe motor circuitry.
 17. The method of claim 14, comprising: measuring,by speed circuitry, a back-electromagnetic force voltage at the motorand at the particular time; and determining, by the speed circuitry, thespeed value associated with the motor at the particular time based onthe back-electromagnetic force voltage.
 18. The method of claim 14,wherein generating the cycle value comprises generating the cycle valuefurther based on a previous speed value associated with the motor at aprevious time that occurred before the particular time and a previouscurrent value associated with the motor at the previous time.